| Texto completo | |
| Autor(es): |
Farooq, Sajid
;
del-Valle, Matheus
;
Santos, Sofia Nascimento dos
;
Bernardes, Emerson Soares
;
Zezell, Denise Maria
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY; v. 310, p. 8-pg., 2024-01-29. |
| Resumo | |
Fourier -transform infrared spectroscopy (FTIR) is a powerful, non-destructive, highly sensitive and a promising analytical technique to provide spectrochemical signatures of biological samples, where markers like carbohydrates, proteins, and phosphate groups of DNA can be recognized in biological micro -environment. However, method of measurements of large cells need an excessive time to achieve high quality images, making its clinical use difficult due to speed of data -acquisition and lack of optimized computational procedures. To address such challenges, Machine Learning (ML) based technologies can assist to assess an accurate prognostication of breast cancer (BC) subtypes with high performance. Here, we applied FTIR spectroscopy to identify breast cancer subtypes in order to differentiate between luminal (BT474) and nonluminal (SKBR3) molecular subtypes. For this reason, we tested multivariate classification technique to extract feature information employing three -dimension (3D) -discriminant analysis approach based on 3D -principle component analysis -linear discriminant analysis (3D-PCA-LDA) and 3D -principal component analysis -quadratic discriminant analysis (3D-PCA-QDA), showing an improvement in sensitivity (98%), specificity (94%) and accuracy (98%) parameters compared to conventional unfolded methods. Our results evidence that 3D-PCALDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment. (AU) | |
| Processo FAPESP: | 21/00633-0 - Processamento e análise de sinais hiperespectrais aplicados ao diagnóstico histopatológico |
| Beneficiário: | Luciano Bachmann |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 17/50332-0 - Capacitação científica, tecnológica e em infraestrutura em radiofármacos, radiações e empreendedorismo a serviço da saúde (PDIp) |
| Beneficiário: | Marcelo Linardi |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa Modernização de Institutos Estaduais de Pesquisa |